Results 21 to 30 of about 13,423,347 (294)
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification
Sensors, 2021 Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions.
Bo Jin +6 more
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Synthesis of a novel photoactivatable glucosylceramide cross-linker
Journal of Lipid Research, 2016 The biosynthesis of glucosylceramide (GlcCer) is a key rate-limiting step in complex glycosphingolipid (GSL) biosynthesis. To further define interacting partners of GlcCer, we have made a cleavable, biotinylated, photoreactive GlcCer analog in which the ...
Monique Budani +3 more
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Exact Learning Augmented Naive Bayes Classifier
Entropy, 2021 Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal ...
Shouta Sugahara, Maomi Ueno
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Improving Bayesian Network Structure Learning in the Presence of Measurement Error
, 2022 Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables.
Liu, Y, Constantinou, AC, Guo, Z
core +1 more source
Learning with structured sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009 This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Junzhou Huang +2 more
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An Active Structure that Learns [PDF]
Journal of Computing in Civil Engineering, 2005 Tensegrity structures are composed of cables and struts that become stable through self stress. They are good candidates for implementation of active structural control because their flexibility may mean that they cannot meet serviceability criteria. Changes to the self stress influence the form of the structure.
Domer, B., Smith, I.F.C.
openaire +2 more sources
Applied Sciences, 2019 This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been
Kang-moon Park +2 more
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Learning Bayesian Networks That Enable Full Propagation of Evidence
IEEE Access, 2020 This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent.
Anthony C. Constantinou
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Remote Sensing, 2023 The reconstruction of 3D shapes from a single view has been a longstanding challenge. Previous methods have primarily focused on learning either geometric features that depict overall shape contours but are insufficient for occluded regions, local ...
Guoqing Gao +5 more
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